Our Faculty

Ramesh Manyam

Assistant Research Professor
Rollins Endowed Faculty Scholar
Department of Biostatistics and Bioinformatics
Rameshbabu Manyam
PUBLICATIONS
PUBMED

Bio

Dr. Manyam is a trained computer scientist whose research focuses on artificial intelligence (AI), machine learning (ML), data science, large-scale healthcare data analytics, risk modeling, and software application development. He has more than two decades of experience in computational and data-driven research, including analyzing longitudinal electronic health record (EHR), clinical, laboratory, survey, biospecimen, and instrument-generated data, as well as developing custom databases, software applications, and data visualization platforms.

His current research focuses on developing scalable, portable, and interpretable AI/ML approaches for predicting clinically important outcomes. His methodological interests include longitudinal and time-series analysis, feature engineering and selection, ensemble and deep learning, survival and time-to-event modeling, and interpretable AI. A major area of his work involves developing predictive models for cardiovascular and surgical outcomes, particularly hospital readmission and failure-to-rescue after coronary artery bypass grafting (CABG). His collaborative research also applies AI/ML methods to maternal morbidity and mortality, interhospital transfer and associated outcomes among older adults, suicide risk, and other perioperative outcomes. He is also interested in translating AI into practical applications, including customized AI-driven educational and conversational tools.

Dr. Manyam has extensive hands-on experience with large-scale healthcare data, including Epic EHR data, the Emory clinical data warehouse, and national clinical and surgical databases. He currently leads the Applied Artificial Intelligence and Data Translation concentration of the Doctor of Public Health (DrPH) program at Emory University. Through his research, teaching, and collaborative work, he focuses on developing rigorous, interpretable, and scalable AI and data science approaches that translate complex health data into actionable knowledge and tools for improving health outcomes.

Recent honors and awards

Areas of Interest

  • Data Science
  • High Performance Computing
  • Longitudinal Analysis
  • Machine Learning
  • Risk Assessment
  • Survival Analysis
  • Cardiovascular Diseases
  • Bioinformatics
  • Artificial Intelligence
  • Mental Health
  • Health Informatics
  • Maternal and Child Health

Education

  • PhD, Georgia State University, Atlanta, GA, USA
  • MS, Georgia State University, Atlanta, GA, USA
  • MTech, Indian Institute of Technology, Banaras Hindu University, Varanasi, India
  • BTech, National Institute of Technology, Warangal, India

Courses Taught

  • BIOS 585 - Python Programming
  • DATA 521 - Database Development for PH

Affiliations

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